Paper
1 May 1994 Development of a recursive zero annihilator periodic (ZAP) controller with specific applications in flexible space structures
Amy M. Jakubowski, John J. Helferty, David S. Bayard
Author Affiliations +
Abstract
When continuous-time systems are discretized in the digital controller design process, it is often the case that unstable discrete-time zeros (i.e., zeros outside the unit circle in the Z- plane) result regardless of whether or not there are unstable zeros in the original continuous- time plant. Such a system is recognized as being nonminimum phase. Unfortunately, many design techniques in adaptive control are dependent upon pole-zero cancellations and stable plant invertibility and, therefore, cannot be utilized when the plant is nonminimum phase. In this research, a matrix parameter recursive least squares adaptation law is developed for the zero annihilator periodic (ZAP) controller first introduced by Bayard and later extended by Jakubowski. This direct adaptive control scheme allows for the construction of an optimal set of matrix controller gains that place the transmission zeros of the system at the origin, alleviating the nonminimum phase condition, and force the system output to track a desired reference signal. Simulations are presented that demonstrate the performance of the adaptive ZAP controller on a 12-state, 2-input, 2-output partial model of one of the Astrex struts, where the model of the particular strut exhibits nonminimum phase characteristics.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Amy M. Jakubowski, John J. Helferty, and David S. Bayard "Development of a recursive zero annihilator periodic (ZAP) controller with specific applications in flexible space structures", Proc. SPIE 2192, Smart Structures and Materials 1994: Mathematics and Control in Smart Structures, (1 May 1994); https://doi.org/10.1117/12.174210
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Cited by 2 scholarly publications.
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KEYWORDS
Matrices

Systems modeling

Adaptive control

Actuators

Control systems

Sensors

Device simulation

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